{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install pandas\n",
    "%pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.layers import *\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.io import loadmat\n",
    "from scipy.io import savemat\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import LinearLocator\n",
    "\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import seed\n",
    "seed(0)\n",
    "tf.random.set_seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Define Something in Advance\n",
    "u(x):exact solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def U(x):\n",
    "    return np.exp(-x)*np.sin(m*np.pi*x**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Initialize the Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_domain = np.linspace(0, np.pi, 200)\n",
    "delta = x_domain[1] - x_domain[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Define Physical Info."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    suppose: x = x[0]; u_x = x[1]; dudx = x[2]; du2dx = x[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "governing_equation_components = []\n",
    "governing_equation_components.append(Lambda(lambda x: l*x[2]))\n",
    "governing_equation_components.append(Lambda(lambda x: g*tf.sin(x[2])))\n",
    "governing_equation_components.append(Lambda(lambda x: x[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "governing_equation_mask = x_domain*0 + 1\n",
    "governing_equation_mask[0] = 0\n",
    "governing_equation_mask[-1] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = 1\n",
    "g = 1\n",
    "m = 1\n",
    "fx = governing_equation_mask*(\n",
    "           l*(2*m*np.pi*x_domain*np.exp(-x_domain)*np.cos(m*np.pi*x_domain**2)-np.exp(-x_domain)*np.sin(m*np.pi*x_domain**2)) + \n",
    "           g*np.sin(2*m*np.pi*x_domain*np.exp(-x_domain)*np.cos(m*np.pi*x_domain**2)-np.exp(-x_domain)*np.sin(m*np.pi*x_domain**2)) + \n",
    "           np.exp(-x_domain)*np.sin(m*np.pi*x_domain**2)    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimate_equation_form = False\n",
    "equation_component_combination = [1.0,1.0,1.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Define the Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    suppose: x = x[0]; u_x = x[1]; dudx = x[2]; du2dx = x[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_components = []\n",
    "observation_components.append(Lambda(lambda x: x[1]))\n",
    "observation_components.append(Lambda(lambda x: x[2]))\n",
    "observation_components.append(Lambda(lambda x: x[3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    format: [x,combination,value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_data = []\n",
    "observation_data.append([0,[1,0,0],U(0)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Define PICN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[-1.0/(delta)]],[[1.0/(delta)]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_2_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[1.0/(delta**2)]],[[-2.0/(delta**2)]],[[1.0/(delta**2)]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = [keras.layers.Input(shape=(1,1)),keras.layers.Input(shape=(len(x_domain),1))]\n",
    "\n",
    "hidden_field = keras.layers.Conv1DTranspose(filters=1, \n",
    "                                            kernel_size=len(x_domain)+4, \n",
    "                                            activation='linear')(inputs[0])\n",
    "coordinates = inputs[1]\n",
    "field = keras.layers.Conv1D(filters=1, \n",
    "                            kernel_size=3, \n",
    "                            padding='valid', \n",
    "                            activation='linear')(hidden_field)\n",
    "gradient_field = keras.layers.Conv1D(filters=1, \n",
    "                                     kernel_size=2, \n",
    "                                     padding='valid',\n",
    "                                     use_bias=False,\n",
    "                                     trainable=False,\n",
    "                                     kernel_initializer=gradient_kernal_init)(field)\n",
    "gradient_2_field = keras.layers.Conv1D(filters=1, \n",
    "                                       kernel_size=3, \n",
    "                                       padding='valid',\n",
    "                                       use_bias=False,\n",
    "                                       trainable=False,\n",
    "                                       kernel_initializer=gradient_2_kernal_init)(field)\n",
    "phycial_fields = [coordinates,field[:,1:-1,:],gradient_field[:,1:,:],gradient_2_field]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "if estimate_equation_form==True:\n",
    "    tf_governing_equation_components = [component(phycial_fields) for component in governing_equation_components]\n",
    "    concat_equation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_governing_equation_components)\n",
    "    governing_equation = keras.layers.Conv1D(filters=1, \n",
    "                                             kernel_size=1, \n",
    "                                             padding='valid',\n",
    "                                             use_bias=False)(concat_equation_components)\n",
    "else:\n",
    "    tf_weighted_governing_equation_components = [weight*component(phycial_fields) for [weight,component] in zip(equation_component_combination,governing_equation_components)]\n",
    "    concat_weighted_equation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_weighted_governing_equation_components)\n",
    "    governing_equation = Lambda(lambda x: tf.reduce_sum(x,axis=-1,keepdims=True))(concat_weighted_equation_components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_observation_components = [component(phycial_fields) for component in observation_components]\n",
    "observation_list = []\n",
    "for data in observation_data:\n",
    "    if data[0] <= x_domain[0]:\n",
    "        left_x_position_index = 0\n",
    "        right_x_position_index = 1\n",
    "        left_x_position_weight = 1\n",
    "        right_x_position_weight = 0\n",
    "    elif data[0] >= x_domain[-1]:\n",
    "        left_x_position_index = -2\n",
    "        right_x_position_index = -1\n",
    "        left_x_position_weight = 0\n",
    "        right_x_position_weight = 1\n",
    "    else:\n",
    "        left_x_position_index = int(np.round((data[0] - x_domain[0])/delta))\n",
    "        right_x_position_index = int(np.round(left_x_position_index + 1))\n",
    "        left_x_position_weight = 1-(data[0] - (x_domain[0]+delta*left_x_position_index))/delta\n",
    "        right_x_position_weight = 1-left_x_position_weight\n",
    "        \n",
    "    left_position_observation_components = [component[:,left_x_position_index,:] for component in tf_observation_components]\n",
    "    right_position_observation_components = [component[:,right_x_position_index,:] for component in tf_observation_components]\n",
    "    \n",
    "    concat_weighted_left_position_observation_components = Lambda(lambda x: tf.concat(x,axis=-1))([weight*component for [weight,component] in zip(data[1],left_position_observation_components)])\n",
    "    concat_weighted_right_position_observation_components = Lambda(lambda x: tf.concat(x,axis=-1))([weight*component for [weight,component] in zip(data[1],right_position_observation_components)])\n",
    "    \n",
    "    left_observation = Lambda(lambda x: tf.reduce_sum(x,axis=-1,keepdims=True))(concat_weighted_left_position_observation_components)\n",
    "    right_observation = Lambda(lambda x: tf.reduce_sum(x,axis=-1,keepdims=True))(concat_weighted_right_position_observation_components)\n",
    "    \n",
    "    observation_list.append(left_x_position_weight*left_observation + right_x_position_weight*right_observation)\n",
    "    \n",
    "observations = Lambda(lambda x: tf.concat(x,axis=-1))(observation_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pde_model = keras.Model(inputs=inputs, outputs=phycial_fields)\n",
    "pde_model_train = keras.Model(inputs=inputs, outputs=[governing_equation,observations])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)        [(None, 1, 1)]               0         []                            \n",
      "                                                                                                  \n",
      " conv1d_transpose (Conv1DTr  (None, 204, 1)               205       ['input_1[0][0]']             \n",
      " anspose)                                                                                         \n",
      "                                                                                                  \n",
      " conv1d (Conv1D)             (None, 202, 1)               4         ['conv1d_transpose[0][0]']    \n",
      "                                                                                                  \n",
      " conv1d_1 (Conv1D)           (None, 201, 1)               2         ['conv1d[0][0]']              \n",
      "                                                                                                  \n",
      " input_2 (InputLayer)        [(None, 200, 1)]             0         []                            \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem (  (None, 200, 1)               0         ['conv1d[0][0]']              \n",
      " SlicingOpLambda)                                                                                 \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_1  (None, 200, 1)               0         ['conv1d_1[0][0]']            \n",
      "  (SlicingOpLambda)                                                                               \n",
      "                                                                                                  \n",
      " conv1d_2 (Conv1D)           (None, 200, 1)               3         ['conv1d[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 214 (856.00 Byte)\n",
      "Trainable params: 209 (836.00 Byte)\n",
      "Non-trainable params: 5 (20.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def model_print_functoin(s):\n",
    "    with open('model_summary.txt','a') as f:\n",
    "        print(s, file=f)\n",
    "\n",
    "pde_model.summary(print_fn=model_print_functoin)\n",
    "pde_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<KerasTensor: shape=(None, 1, 1) dtype=float32 (created by layer 'input_1')>,\n",
       " <KerasTensor: shape=(None, 200, 1) dtype=float32 (created by layer 'input_2')>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pde_model_train.input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<KerasTensor: shape=(None, 200, 1) dtype=float32 (created by layer 'lambda_7')>,\n",
       " <KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'lambda_12')>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pde_model_train.output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Prepare the Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "unit_constant = np.asarray([[[1.0]]],dtype=np.float32)\n",
    "training_input_data_0 = np.repeat(unit_constant, 1, axis=0)\n",
    "print(training_input_data_0.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 200, 1)\n"
     ]
    }
   ],
   "source": [
    "training_input_data_1 = np.expand_dims(x_domain.astype(np.float32),axis=[0,-1])\n",
    "print(training_input_data_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 200, 1)\n"
     ]
    }
   ],
   "source": [
    "training_label_data_0 = np.expand_dims(fx,axis=(0,-1))\n",
    "print(training_label_data_0.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1)\n"
     ]
    }
   ],
   "source": [
    "training_label_data_1 = np.expand_dims(np.asarray([data[2] for data in observation_data]),axis=[0])\n",
    "print(training_label_data_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_input_data = [training_input_data_0,training_input_data_1]\n",
    "training_label_data = [training_label_data_0,training_label_data_1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['loss', 'lambda_7_loss', 'lambda_12_loss']\n"
     ]
    }
   ],
   "source": [
    "pde_model_train.compile(optimizer=keras.optimizers.Adam(), loss=\"mse\")\n",
    "pde_model_train.save_weights('picn_initial_weights.h5')\n",
    "temp_history = pde_model_train.fit(x=training_input_data, y=training_label_data, epochs=1, verbose=0)\n",
    "history_keys = []\n",
    "for key in temp_history.history.keys():\n",
    "    history_keys.append(key)\n",
    "print(history_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def record_predictions():\n",
    "    [_,ux, dudx, d2udx2] = pde_model.predict(training_input_data)\n",
    "    ux_list.append(ux[0,:,0])\n",
    "    dudx_list.append(dudx[0,:,0])\n",
    "    d2udx2_list.append(d2udx2[0,:,0])\n",
    "\n",
    "class Per_X_Epoch_Record(tf.keras.callbacks.Callback):\n",
    "    def __init__(self, record_interval, verbose=1):\n",
    "        super(tf.keras.callbacks.Callback, self).__init__()\n",
    "        self.total_loss = history_keys[0]\n",
    "        self.domain_loss = history_keys[1]\n",
    "        self.bdc_loss = history_keys[2]\n",
    "        self.previous_total_loss = 9999999\n",
    "        self.record_interval = record_interval;\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        \n",
    "        if epoch%self.record_interval == 0:\n",
    "            \n",
    "            current_total_loss = logs.get(self.total_loss)\n",
    "            current_domain_loss = logs.get(self.domain_loss)\n",
    "            current_bdc_loss = logs.get(self.bdc_loss)\n",
    "            \n",
    "            epoch_number_list.append(epoch)\n",
    "            total_loss_list.append(current_total_loss)\n",
    "            domain_loss_list.append(current_domain_loss)\n",
    "            boundary_loss_list.append(current_bdc_loss)\n",
    "        \n",
    "            if current_total_loss < self.previous_total_loss:\n",
    "                self.previous_total_loss = current_total_loss\n",
    "                pde_model_train.save_weights('picn_best_weights.h5')\n",
    "            \n",
    "            if self.verbose > 0:\n",
    "                print(\"epoch: {:10.5f} | total_loss: {:10.5f} | domain_loss: {:10.5f} | bdc_loss: {:10.5f}\".format(epoch,current_total_loss,current_domain_loss,current_bdc_loss))\n",
    "        \n",
    "            # evaluate the errors in f-domain\n",
    "            record_predictions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [\n",
    "    Per_X_Epoch_Record(record_interval=200,verbose=1),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:    0.00000 | total_loss:    1.29920 | domain_loss:   12.97040 | bdc_loss:    0.00240\n",
      "1/1 [==============================] - 0s 66ms/step\n",
      "epoch:  200.00000 | total_loss:    0.07145 | domain_loss:    0.71442 | bdc_loss:    0.00001\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch:  400.00000 | total_loss:    0.03100 | domain_loss:    0.30994 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch:  600.00000 | total_loss:    0.03025 | domain_loss:    0.30246 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch:  800.00000 | total_loss:    0.02988 | domain_loss:    0.29883 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 1000.00000 | total_loss:    0.00065 | domain_loss:    0.00654 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 1200.00000 | total_loss:    0.00048 | domain_loss:    0.00481 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 1400.00000 | total_loss:    0.00025 | domain_loss:    0.00250 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "epoch: 1600.00000 | total_loss:    0.00020 | domain_loss:    0.00197 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 1800.00000 | total_loss:    0.00017 | domain_loss:    0.00168 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 2000.00000 | total_loss:    0.00016 | domain_loss:    0.00160 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 2200.00000 | total_loss:    0.00015 | domain_loss:    0.00147 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 2400.00000 | total_loss:    0.00013 | domain_loss:    0.00132 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 2600.00000 | total_loss:    0.00013 | domain_loss:    0.00126 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 2800.00000 | total_loss:    0.00012 | domain_loss:    0.00123 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 3000.00000 | total_loss:    0.00016 | domain_loss:    0.00155 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "epoch: 3200.00000 | total_loss:    0.00013 | domain_loss:    0.00129 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 3400.00000 | total_loss:    0.00012 | domain_loss:    0.00119 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 3600.00000 | total_loss:    0.00014 | domain_loss:    0.00139 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 3800.00000 | total_loss:    0.00012 | domain_loss:    0.00122 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 4000.00000 | total_loss:    0.00018 | domain_loss:    0.00181 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 4200.00000 | total_loss:    0.00013 | domain_loss:    0.00128 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 4400.00000 | total_loss:    0.00015 | domain_loss:    0.00149 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 4600.00000 | total_loss:    0.00014 | domain_loss:    0.00140 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 4800.00000 | total_loss:    0.00015 | domain_loss:    0.00149 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 5000.00000 | total_loss:    0.00013 | domain_loss:    0.00126 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 5200.00000 | total_loss:    0.00013 | domain_loss:    0.00129 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 5400.00000 | total_loss:    0.00012 | domain_loss:    0.00121 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 5600.00000 | total_loss:    0.00014 | domain_loss:    0.00135 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 5800.00000 | total_loss:    0.00016 | domain_loss:    0.00161 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 6000.00000 | total_loss:    0.00016 | domain_loss:    0.00160 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 6200.00000 | total_loss:    0.00014 | domain_loss:    0.00143 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 6400.00000 | total_loss:    0.00014 | domain_loss:    0.00141 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 6600.00000 | total_loss:    0.00015 | domain_loss:    0.00147 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 6800.00000 | total_loss:    0.00013 | domain_loss:    0.00128 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 7000.00000 | total_loss:    0.00016 | domain_loss:    0.00162 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 7200.00000 | total_loss:    0.00013 | domain_loss:    0.00129 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 7400.00000 | total_loss:    0.00017 | domain_loss:    0.00163 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 7600.00000 | total_loss:    0.00016 | domain_loss:    0.00158 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 7800.00000 | total_loss:    0.00012 | domain_loss:    0.00123 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 8000.00000 | total_loss:    0.00013 | domain_loss:    0.00125 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 8200.00000 | total_loss:    0.00017 | domain_loss:    0.00170 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 8400.00000 | total_loss:    0.00013 | domain_loss:    0.00126 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 8600.00000 | total_loss:    0.00013 | domain_loss:    0.00132 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 8800.00000 | total_loss:    0.00016 | domain_loss:    0.00157 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 9000.00000 | total_loss:    0.00012 | domain_loss:    0.00125 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 9200.00000 | total_loss:    0.00013 | domain_loss:    0.00126 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 9400.00000 | total_loss:    0.00015 | domain_loss:    0.00152 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 9600.00000 | total_loss:    0.00013 | domain_loss:    0.00133 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 9800.00000 | total_loss:    0.00016 | domain_loss:    0.00159 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 10000.00000 | total_loss:    0.00014 | domain_loss:    0.00142 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 10200.00000 | total_loss:    0.00022 | domain_loss:    0.00222 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 10400.00000 | total_loss:    0.00013 | domain_loss:    0.00133 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 10600.00000 | total_loss:    0.00019 | domain_loss:    0.00183 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 10800.00000 | total_loss:    0.00014 | domain_loss:    0.00136 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 11000.00000 | total_loss:    0.00013 | domain_loss:    0.00133 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 11200.00000 | total_loss:    0.00013 | domain_loss:    0.00133 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 11400.00000 | total_loss:    0.00013 | domain_loss:    0.00125 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 11600.00000 | total_loss:    0.00013 | domain_loss:    0.00126 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 11800.00000 | total_loss:    0.00013 | domain_loss:    0.00129 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 12000.00000 | total_loss:    0.00014 | domain_loss:    0.00135 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 12200.00000 | total_loss:    0.00013 | domain_loss:    0.00133 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 12400.00000 | total_loss:    0.00013 | domain_loss:    0.00130 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 12600.00000 | total_loss:    0.00013 | domain_loss:    0.00134 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 12800.00000 | total_loss:    0.00013 | domain_loss:    0.00125 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 13000.00000 | total_loss:    0.00012 | domain_loss:    0.00124 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 13200.00000 | total_loss:    0.00014 | domain_loss:    0.00136 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 13400.00000 | total_loss:    0.00013 | domain_loss:    0.00125 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 13600.00000 | total_loss:    0.00014 | domain_loss:    0.00135 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 13800.00000 | total_loss:    0.00017 | domain_loss:    0.00169 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 14000.00000 | total_loss:    0.00013 | domain_loss:    0.00130 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 14200.00000 | total_loss:    0.00012 | domain_loss:    0.00121 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 14400.00000 | total_loss:    0.00013 | domain_loss:    0.00127 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 14600.00000 | total_loss:    0.00014 | domain_loss:    0.00136 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 14800.00000 | total_loss:    0.00013 | domain_loss:    0.00129 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 15000.00000 | total_loss:    0.00014 | domain_loss:    0.00135 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 15200.00000 | total_loss:    0.00012 | domain_loss:    0.00120 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 15400.00000 | total_loss:    0.00016 | domain_loss:    0.00158 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 15600.00000 | total_loss:    0.00013 | domain_loss:    0.00125 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 15800.00000 | total_loss:    0.00012 | domain_loss:    0.00123 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 16000.00000 | total_loss:    0.00013 | domain_loss:    0.00130 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 16200.00000 | total_loss:    0.00013 | domain_loss:    0.00126 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 16400.00000 | total_loss:    0.00014 | domain_loss:    0.00141 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 16600.00000 | total_loss:    0.00015 | domain_loss:    0.00153 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 16800.00000 | total_loss:    0.00015 | domain_loss:    0.00147 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 17000.00000 | total_loss:    0.00014 | domain_loss:    0.00136 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 17200.00000 | total_loss:    0.00013 | domain_loss:    0.00130 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "epoch: 17400.00000 | total_loss:    0.00012 | domain_loss:    0.00124 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 17600.00000 | total_loss:    0.00016 | domain_loss:    0.00154 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 17800.00000 | total_loss:    0.00013 | domain_loss:    0.00132 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 18000.00000 | total_loss:    0.00013 | domain_loss:    0.00133 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 18200.00000 | total_loss:    0.00012 | domain_loss:    0.00125 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 18400.00000 | total_loss:    0.00015 | domain_loss:    0.00150 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 18600.00000 | total_loss:    0.00012 | domain_loss:    0.00121 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 18800.00000 | total_loss:    0.00012 | domain_loss:    0.00123 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19000.00000 | total_loss:    0.00013 | domain_loss:    0.00132 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 19200.00000 | total_loss:    0.00012 | domain_loss:    0.00124 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19400.00000 | total_loss:    0.00013 | domain_loss:    0.00134 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19600.00000 | total_loss:    0.00013 | domain_loss:    0.00130 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19800.00000 | total_loss:    0.00014 | domain_loss:    0.00139 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "Training time cost:   40.93516 seconds\n"
     ]
    }
   ],
   "source": [
    "epoch_number_list = []\n",
    "total_loss_list = []\n",
    "domain_loss_list = []\n",
    "boundary_loss_list = []\n",
    "ux_list = []\n",
    "dudx_list = []\n",
    "d2udx2_list = []\n",
    "pde_model_train.load_weights('picn_initial_weights.h5')\n",
    "pde_model_train.compile(optimizer=keras.optimizers.Adam(learning_rate=0.01), loss=\"mse\", loss_weights = [0.1, 0.9])\n",
    "\n",
    "# Record the training time cost\n",
    "start = time.time()\n",
    "\n",
    "# Training\n",
    "pde_model_train.fit(x=training_input_data, \n",
    "                    y=training_label_data, \n",
    "                    epochs=20000, verbose=0,\n",
    "                    callbacks=callbacks)\n",
    "\n",
    "# Record the training time cost\n",
    "end = time.time()\n",
    "print(\"Training time cost: {:10.5f} seconds\".format(end - start))\n",
    "time_costs_text_file = open(\"time_costs.txt\", \"w\")\n",
    "time_costs_text_file.write(\"Training time cost: {:10.5f} seconds\".format(end - start))\n",
    "time_costs_text_file.close()\n",
    "\n",
    "# Load the best weights\n",
    "pde_model_train.load_weights('picn_best_weights.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. LOSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.DataFrame({'epoch': epoch_number_list, \n",
    "                 'total_loss': total_loss_list, \n",
    "                 'governing_loss': domain_loss_list, \n",
    "                 'boundary_loss': boundary_loss_list})\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot( 'epoch', 'total_loss', data=df, marker='o', markerfacecolor='red', markersize=2, color='skyblue', linewidth=3)\n",
    "plt.plot( 'epoch', 'governing_loss', data=df, marker='', color='green', linewidth=2, linestyle='dashed')\n",
    "plt.plot( 'epoch', 'boundary_loss', data=df, marker='', color='blue', linewidth=2, linestyle='dashed')\n",
    "plt.legend()\n",
    "#plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 12ms/step\n"
     ]
    }
   ],
   "source": [
    "[_, u_pred, dudx_pred, d2udx2_pred] = pde_model.predict(training_input_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Results overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(x_domain,u_pred[0,:,0],label='u_pred')\n",
    "plt.plot(x_domain,U(x_domain),label='truth')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('u_pred')\n",
    "plt.title('u-prediction')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(x_domain,U(x_domain),label='truth')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('dudx_pred')\n",
    "plt.title('dudx-prediction')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(x_domain,u_pred[0,:,0]-U(x_domain),label='error')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('d2udx2_pred')\n",
    "plt.title('d2udx2-prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Save the Process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "picn_process_data = {'x_domain':x_domain,\n",
    "                     'epoch_number_list':np.asarray(epoch_number_list),\n",
    "                     'total_loss_list':np.asarray(total_loss_list),\n",
    "                     'domain_loss_list':np.asarray(domain_loss_list),\n",
    "                     'boundary_loss_list':np.asarray(boundary_loss_list),\n",
    "                     'ux_list':np.vstack(ux_list),\n",
    "                     'dudx_list':np.vstack(dudx_list),\n",
    "                     'd2udx2_list':np.vstack(d2udx2_list)}\n",
    "savemat('picn_process_data.mat', picn_process_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
